{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:20:55.647862Z",
     "start_time": "2023-04-22T12:20:54.968535Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 准备数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:20:55.663373Z",
     "start_time": "2023-04-22T12:20:55.648863Z"
    }
   },
   "outputs": [],
   "source": [
    "x0 = np.linspace(-2, 2, 1000)\n",
    "y0 = 2 * np.sin(x0)\n",
    "y0 += 0.32 * np.random.randn(y0.shape[0]) # 加噪声"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "给x和y加一维"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:20:55.679372Z",
     "start_time": "2023-04-22T12:20:55.666373Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 1)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = x0[:, np.newaxis]\n",
    "y = y0[:, np.newaxis]\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 线性回归损失作为基准（baseline）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:20:55.695404Z",
     "start_time": "2023-04-22T12:20:55.681373Z"
    }
   },
   "outputs": [],
   "source": [
    "def mse(yl, y0):\n",
    "    return ((yl - y0)**2).sum()/yl.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:20:55.711371Z",
     "start_time": "2023-04-22T12:20:55.696372Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "回归方程为: y = -0.02 + 1.31 * x\n"
     ]
    }
   ],
   "source": [
    "b2 = float((len(y0)*(x0*y0).sum(0) - y.sum(0)*x0.sum(0)) /\n",
    "           (len(x0)*(x0**2).sum(0) - (x.sum(0)**2)))\n",
    "b1 = float(y0.mean() - b2 * x0.mean())\n",
    "print('回归方程为: y = {:.2f} + {:.2f} * x'.format(b1, b2))\n",
    "# 数据是标准化的，因此截距项为0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:20:55.727390Z",
     "start_time": "2023-04-22T12:20:55.713370Z"
    }
   },
   "outputs": [],
   "source": [
    "yl = []\n",
    "for i in x0:\n",
    "    yl.append(b1 + b2 * i)\n",
    "yl = np.array(yl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:20:55.743379Z",
     "start_time": "2023-04-22T12:20:55.729372Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "线性回归平均损失:0.2045\n"
     ]
    }
   ],
   "source": [
    "baseline = mse(yl, y0) # type: ignore\n",
    "print('线性回归平均损失:{:.4f}'.format(baseline))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:20:55.918431Z",
     "start_time": "2023-04-22T12:20:55.744380Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x14f93dd1420>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x0, y0, label='原始点')\n",
    "plt.plot(x, yl, 'r', label='最小二乘法回归线, 损失{:.3f}'.format(baseline))\n",
    "plt.legend(fontsize='13')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tensorflow/keras神经网络框架"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:20:58.342215Z",
     "start_time": "2023-04-22T12:20:55.920430Z"
    }
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:20:59.114910Z",
     "start_time": "2023-04-22T12:20:58.343198Z"
    }
   },
   "outputs": [],
   "source": [
    "# type: ignore\n",
    "model_tf = keras.models.Sequential([ \n",
    "    keras.layers.Dense(5, input_shape=[1,], activation='sigmoid'),\n",
    "    keras.layers.Dense(1),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:20:59.130787Z",
     "start_time": "2023-04-22T12:20:59.115892Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 5)                 10        \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 6         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 16\n",
      "Trainable params: 16\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_tf.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:20:59.146831Z",
     "start_time": "2023-04-22T12:20:59.133787Z"
    }
   },
   "outputs": [],
   "source": [
    "model_tf.compile(loss='mse', optimizer=keras.optimizers.SGD(0.01))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:21:05.578232Z",
     "start_time": "2023-04-22T12:20:59.147814Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "22/22 [==============================] - 1s 8ms/step - loss: 1.4838 - val_loss: 6.2583\n",
      "Epoch 2/50\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 1.3168 - val_loss: 6.3711\n",
      "Epoch 3/50\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 1.2212 - val_loss: 6.0765\n",
      "Epoch 4/50\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 1.1385 - val_loss: 5.6717\n",
      "Epoch 5/50\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 1.0598 - val_loss: 5.2560\n",
      "Epoch 6/50\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.9835 - val_loss: 4.8968\n",
      "Epoch 7/50\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.9097 - val_loss: 4.4423\n",
      "Epoch 8/50\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.8386 - val_loss: 4.0308\n",
      "Epoch 9/50\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.7703 - val_loss: 3.6713\n",
      "Epoch 10/50\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.7054 - val_loss: 3.3194\n",
      "Epoch 11/50\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.6436 - val_loss: 2.9543\n",
      "Epoch 12/50\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.5846 - val_loss: 2.6164\n",
      "Epoch 13/50\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.5302 - val_loss: 2.3234\n",
      "Epoch 14/50\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.4796 - val_loss: 2.0399\n",
      "Epoch 15/50\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.4339 - val_loss: 1.8191\n",
      "Epoch 16/50\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.3924 - val_loss: 1.6023\n",
      "Epoch 17/50\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.3553 - val_loss: 1.4037\n",
      "Epoch 18/50\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.3222 - val_loss: 1.2194\n",
      "Epoch 19/50\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.2934 - val_loss: 1.0800\n",
      "Epoch 20/50\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.2675 - val_loss: 0.9433\n",
      "Epoch 21/50\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.2452 - val_loss: 0.8396\n",
      "Epoch 22/50\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.2263 - val_loss: 0.7353\n",
      "Epoch 23/50\n",
      "22/22 [==============================] - 0s 7ms/step - loss: 0.2100 - val_loss: 0.6487\n",
      "Epoch 24/50\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.1961 - val_loss: 0.5814\n",
      "Epoch 25/50\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.1844 - val_loss: 0.5097\n",
      "Epoch 26/50\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.1743 - val_loss: 0.4575\n",
      "Epoch 27/50\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.1661 - val_loss: 0.4126\n",
      "Epoch 28/50\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.1590 - val_loss: 0.3713\n",
      "Epoch 29/50\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.1532 - val_loss: 0.3400\n",
      "Epoch 30/50\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.1482 - val_loss: 0.3132\n",
      "Epoch 31/50\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.1442 - val_loss: 0.2906\n",
      "Epoch 32/50\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.1407 - val_loss: 0.2697\n",
      "Epoch 33/50\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.1379 - val_loss: 0.2490\n",
      "Epoch 34/50\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.1355 - val_loss: 0.2351\n",
      "Epoch 35/50\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.1335 - val_loss: 0.2224\n",
      "Epoch 36/50\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.1319 - val_loss: 0.2123\n",
      "Epoch 37/50\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.1304 - val_loss: 0.2016\n",
      "Epoch 38/50\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.1293 - val_loss: 0.1939\n",
      "Epoch 39/50\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.1281 - val_loss: 0.1860\n",
      "Epoch 40/50\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.1273 - val_loss: 0.1827\n",
      "Epoch 41/50\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.1264 - val_loss: 0.1756\n",
      "Epoch 42/50\n",
      "22/22 [==============================] - 0s 7ms/step - loss: 0.1257 - val_loss: 0.1724\n",
      "Epoch 43/50\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.1253 - val_loss: 0.1670\n",
      "Epoch 44/50\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.1246 - val_loss: 0.1619\n",
      "Epoch 45/50\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.1240 - val_loss: 0.1611\n",
      "Epoch 46/50\n",
      "22/22 [==============================] - 0s 7ms/step - loss: 0.1236 - val_loss: 0.1555\n",
      "Epoch 47/50\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.1233 - val_loss: 0.1542\n",
      "Epoch 48/50\n",
      "22/22 [==============================] - 0s 8ms/step - loss: 0.1228 - val_loss: 0.1512\n",
      "Epoch 49/50\n",
      "22/22 [==============================] - 0s 7ms/step - loss: 0.1225 - val_loss: 0.1504\n",
      "Epoch 50/50\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.1221 - val_loss: 0.1488\n"
     ]
    }
   ],
   "source": [
    "history = model_tf.fit(x, y, epochs=50, validation_split=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:21:05.783017Z",
     "start_time": "2023-04-22T12:21:05.579233Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 0s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "y_tf = model_tf.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:21:05.798990Z",
     "start_time": "2023-04-22T12:21:05.784992Z"
    }
   },
   "outputs": [],
   "source": [
    "keras_mse = mse(y_tf, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:21:06.003129Z",
     "start_time": "2023-04-22T12:21:05.800989Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(history.history).plot(figsize=(8, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:21:06.284975Z",
     "start_time": "2023-04-22T12:21:06.006127Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1526fb163b0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x0, y0, c='gray', label='原始点')\n",
    "plt.plot(x0, yl, 'r', label='最小二乘法回归线, MSE为 {:.3f}'.format(baseline))\n",
    "plt.plot(x0, y_tf, 'green', label='keras神经网络, MSE为 {:.3f}'.format(keras_mse))\n",
    "plt.legend(fontsize='10')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 手搓神经网络框架"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:21:06.315964Z",
     "start_time": "2023-04-22T12:21:06.285947Z"
    }
   },
   "outputs": [],
   "source": [
    "class BP():\n",
    "    def __init__(self) -> None:\n",
    "        pass\n",
    "\n",
    "    def activate(self, t, activator='sigmoid'):\n",
    "        '''\n",
    "        激活函数， sigmoid 或 relu\n",
    "        '''\n",
    "        if activator == 'sigmoid':\n",
    "            return 1/(1+np.e**(-t))\n",
    "        if activator == 'relu':\n",
    "            if t > 0:\n",
    "                return t\n",
    "            else:\n",
    "                return 0\n",
    "\n",
    "    def input_forward_hidden(self, x, activator='sigmoid'): # 输入层到隐藏层\n",
    "        ''' \n",
    "        y11 = w1x1 + b1 \\\\\n",
    "        输出大小 (5, 1) -> (神经元数量, x数量) \\\\\n",
    "        输出 y12 (y11在反向传播用不到)\n",
    "        '''\n",
    "        x = x[:, np.newaxis]\n",
    "        y11 = self.w1 @ x + self.b1 # 叉乘**\n",
    "        y12 = self.activate(y11, activator)\n",
    "        return y12\n",
    "\n",
    "    def hidden_forward_output(self, y12, activator='sigmoid'):  # 隐藏层到输出层\n",
    "        ''' \n",
    "        y21 = y12 * w2 + b2 \\\\\n",
    "        大小 (1, 1) -> (神经元数量, y数量) \\\\\n",
    "        输出 y21, y21(最终输出)\n",
    "        '''\n",
    "        y21 = self.w2 @ y12 + self.b2 # 叉乘**\n",
    "        return y21\n",
    "\n",
    "    def metrics(self, y_p, y_t, loss='mse') -> float: # type: ignore\n",
    "        '''\n",
    "        计算误差\n",
    "        '''\n",
    "        if loss == 'mse':  # 均方差\n",
    "            return (((y_p - y_t)**2).sum()/2)\n",
    "\n",
    "    # 下面是四个参数的反向传播更新，公式是手推链式求导直接写的**\n",
    "    def update_w2(self, yt, y21, y12, lr=0.02):\n",
    "        '''\n",
    "        更新输出层权重,先是输出层w2\n",
    "        '''\n",
    "        delta_w2 = (y21 - yt).sum() * y12.T\n",
    "        return self.w2 - lr * delta_w2\n",
    "\n",
    "    def update_b2(self, yt, y21, lr=0.02):\n",
    "        delta_b2 = (y21 - yt).sum()\n",
    "        return self.b2 - lr * delta_b2\n",
    "    \n",
    "    def update_w1(self, yt, y21, y12, x, lr=0.02):\n",
    "        delta_w1 = (y21 - yt).sum() * self.w2.T * y12 * (1 - y12) * x.T\n",
    "        return self.w1 - lr * delta_w1\n",
    "    \n",
    "    def update_b1(self, yt, y21, y12, lr=0.02):\n",
    "        delta_b1 = (y21 - yt).sum() * self.w2.T * y12 * (1 - y12)\n",
    "        return self.b1 - lr * delta_b1\n",
    "    \n",
    "    def fit(self, x, y):\n",
    "        self.w1 = np.random.randn(5, 1) # (本层神经元数量, 一次输入一个)，实际上可以用batch一次多个**\n",
    "        self.b1 = np.random.randn(5, 1) # 本层神经元数量, 一维神经网络就一层**\n",
    "        self.w2 = np.random.randn(1, self.w1.shape[0]) # 本层神经元数量，实例输入量(上一层神经元数量)**\n",
    "        self.b2 = np.random.randn(1, 1) # 本层神经元数量, 一维神经网络就一层**\n",
    "        self.y = y\n",
    "        self.x = x\n",
    "        self.mse = 0\n",
    "        \n",
    "        # 统计损失\n",
    "        self.list_mse = []\n",
    "        \n",
    "    def train(self, epochs:int, lr=0.02, activator='sigmoid', loss_type='mse', target_error=0.00):\n",
    "        for _ in range(epochs): \n",
    "            for xs, yt in zip(self.x, self.y): # x_sample, y_true(label)\n",
    "                y12 = self.input_forward_hidden(xs, activator)\n",
    "                y21 = self.hidden_forward_output(y12, activator)\n",
    "                self.w2 = self.update_w2(yt, y21, y12, lr)\n",
    "                self.b2 = self.update_b2(yt, y21, lr)\n",
    "                self.w1 = self.update_w1(yt, y21, y12, xs, lr)\n",
    "                self.b1 = self.update_b1(yt, y21, y12, lr)\n",
    "                self.mse = self.metrics(y21, yt, loss_type) # type: ignore\n",
    "            self.list_mse.append(self.mse)\n",
    "                \n",
    "            if self.mse <= target_error:\n",
    "                print('✅ 共{:d}轮, 达到目标损失, 最终损失E={:.5f}'.format(_+1, self.mse))\n",
    "                break\n",
    "            \n",
    "            if _ > 15 and np.mean(self.list_mse[-15: -1]) < self.list_mse[-1]:\n",
    "                print('✅ 共{:d}轮, 损失反向增大，自动停止, 最终损失E={:.5f}'.format(_+1, self.mse))\n",
    "                break\n",
    "            \n",
    "            elif _ == epochs - 1 and self.mse > target_error:\n",
    "                print('❌ 共{:d}轮, 最终损失E={:.5f}, 未达到目标损失 {:.5f}'.format(_+1, self.mse, target_error))\n",
    "                break\n",
    "            \n",
    "                \n",
    "    def fit_transform(self, x, y, epochs:int, lr=0.01, activator='sigmoid', loss_type='mse', target_error=0.00):\n",
    "        '''\n",
    "        epochs: 最大轮次, 每一轮次过一次全部数据 \\\\\n",
    "        lr: 学习率, 默认0.01 \\\\\n",
    "        activator: 'sigmoid' 或 'relu'(尚未配适反向传播, 勿用) \\\\\n",
    "        loss_type: 损失类型, 目前仅有 \"mse\"\n",
    "        '''\n",
    "        self.fit(x, y)\n",
    "        self.train(epochs, lr, activator, loss_type, target_error)\n",
    "        \n",
    "    def predict(self, new_x):\n",
    "        y_lst = []\n",
    "        for i in new_x:\n",
    "            y0 = self.input_forward_hidden(i)\n",
    "            y1 = self.hidden_forward_output(y0)\n",
    "            y_lst.append(y1)\n",
    "        y_lst = np.array(y_lst)\n",
    "        y_lst = y_lst.reshape(y_lst.shape[0])\n",
    "        return y_lst\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:21:06.331944Z",
     "start_time": "2023-04-22T12:21:06.317964Z"
    }
   },
   "outputs": [],
   "source": [
    "model = BP()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:21:07.797004Z",
     "start_time": "2023-04-22T12:21:06.333947Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ 共18轮, 损失反向增大，自动停止, 最终损失E=0.02006\n"
     ]
    }
   ],
   "source": [
    "model.fit_transform(x, y, 100, lr=0.001, )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 损失情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:21:08.033117Z",
     "start_time": "2023-04-22T12:21:07.800005Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1526fc447f0>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('损失情况', fontsize=15)\n",
    "plt.ylabel('损失', fontsize=15)\n",
    "plt.xlabel('训练回合', fontsize=15)\n",
    "plt.grid()\n",
    "plt.plot(model.list_mse, label='神经网络')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:21:08.049114Z",
     "start_time": "2023-04-22T12:21:08.034115Z"
    }
   },
   "outputs": [],
   "source": [
    "yp = model.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:21:08.064119Z",
     "start_time": "2023-04-22T12:21:08.050114Z"
    }
   },
   "outputs": [],
   "source": [
    "bp_mse = mse(yp, y0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:21:08.365294Z",
     "start_time": "2023-04-22T12:21:08.065620Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1526fc85ba0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.text(0, -2.5, 'y=2sinx', fontsize=15)\n",
    "plt.scatter(x, y, s=3, c='gray', label='原散点')\n",
    "plt.plot(x0, yl, 'r', label='最小二乘法回归线, mse = {:.3f}'.format(baseline))\n",
    "plt.plot(x0, y_tf, 'green', label='keras神经网络, mse = {:.3f}'.format(keras_mse))\n",
    "plt.plot(x, yp, c='b', label='手搓神经网络, mse = {:.3f}'.format(bp_mse))\n",
    "plt.legend(fontsize=10)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
